For timberland companies, governments or research institutions, it is often important to be able to accurately classify the ages of trees in a forest without having to physically go and inspect the forest. For example, access to remote forest lands may be difficult or the forest may be so large that it is not cost effective to send crews out to survey the entire site. As a result, satellite or aerial images of the forest are often used to classify the ages of the trees in the forest.
One conventional method of tree age classification using satellite images involves detecting differences in a number of time-spaced images to determine when a forest area is harvested and tracking the corresponding area in a number of the later images to determine how the trees are growing. From the analysis of the changes between images, the age of the trees can be determined. Such a method is both time consuming and requires a certain amount of human insight thereby making it difficult to automate.
Given these problems, there is a need for a system and method for automatically classifying the ages of trees using remotely obtained images in a way that is more efficient and can be automated.